Model comparison
GPT-5.6 Terra vs MiMo-V2.5
GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 43.4 on the Noometry Index. MiMo-V2.5 costs 26× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Last verified . 23 shared benchmarks.
Summary
- They share 23 benchmarks with published results for both. GPT-5.6 Terra scores higher in 9 categories and MiMo-V2.5 in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Terra leads 81.6 to 36.8.
- The biggest single-benchmark swing is ProofBench: 74% for GPT-5.6 Terra and 16% for MiMo-V2.5.
- MiMo-V2.5 is cheaper at $0.14 / $0.28 per million input/output tokens, against $2 / $12 for GPT-5.6 Terra.
- GPT-5.6 Terra accepts more context: 1.05M tokens versus 1.05M.
- MiMo-V2.5 has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Terra | MiMo-V2.5 | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 59.2 | 43.4 |
| Released | 2026-07-09 | 2026-04-22 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 1.05M |
| Max output | 128K | 131K |
| Input $ / M tokens | $2 | $0.14 |
| Output $ / M tokens | $12 | $0.28 |
| Results tracked | 52 | 23 |
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Category by category
Coding GPT-5.6 Terra leads
GPT-5.6 Terra: 57.7 (#19), MiMo-V2.5: 43.9 (#81)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5 |
|---|---|---|
| LMArena WebDev | 1522 | 1438 |
| SciCode | 55% | 43.1% |
| LMArena Coding | 1484 | 1469 |
| ALE-Bench | 1,951 | 513.95 |
| DeepSWE | 69.6% | — |
| FrontierCode | 41.3% | — |
| CursorBench | 41.3% | — |
| WeirdML | 78.3% | — |
Agentic & Tool Use Not comparable
GPT-5.6 Terra: 40.1 (#25), MiMo-V2.5: —
| Benchmark | GPT-5.6 Terra | MiMo-V2.5 |
|---|---|---|
| APEX-Agents | 58.2% | — |
| BALROG | 53.2% | — |
| GDP.pdf | 24.7% | — |
| Vending-Bench 2 | 7,343 | — |
Reasoning GPT-5.6 Terra leads
GPT-5.6 Terra: 60.7 (#21), MiMo-V2.5: 28.6 (#101)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5 |
|---|---|---|
| CritPt | 30% | 3.7% |
| LMArena Hard Prompts | 1468 | 1450 |
| ARC-AGI-2 | 83.9% | — |
| SimpleBench | 48.9% | — |
| Kagi LLM Benchmark | 51.3% | — |
| NYT Connections (extended) | 78.4% | — |
| ARC-AGI-1 | 96.5% | — |
| Chess Puzzles | 54% | — |
| Mystery Game Puzzles | 35% | — |
| DTBench | 93.3% | — |
| LMCA | 55% | — |
| Surface Evolver Bench | 83.8% | — |
| Epoch Capabilities Index | 159.62 | — |
Math GPT-5.6 Terra leads
GPT-5.6 Terra: 81.6 (#12), MiMo-V2.5: 36.8 (#163)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5 |
|---|---|---|
| ProofBench | 74% | 16% |
| LMArena Math | 1466 | 1436 |
| FrontierMath (Tiers 1-3) | 86% | — |
| FrontierMath Tier 4 | 70.7% | — |
| OTIS Mock AIME 2024-2025 | 99.7% | — |
Knowledge GPT-5.6 Terra leads
GPT-5.6 Terra: 61.2 (#30), MiMo-V2.5: 40.8 (#115)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5 |
|---|---|---|
| LMArena Expert | 1492 | 1460 |
| GPQA Diamond | 93.3% | — |
| SimpleQA Verified | 43.2% | — |
Multimodal GPT-5.6 Terra leads
GPT-5.6 Terra: 47.3 (#11), MiMo-V2.5: 39.8 (#54)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5 |
|---|---|---|
| LMArena Vision | 1271 | 1247 |
| Blueprint-Bench 2 | 30.8% | — |
| Furniture Assembly | 54.2% | — |
| LMArena Document | 1472 | — |
Multilingual GPT-5.6 Terra leads
GPT-5.6 Terra: 54.4 (#44), MiMo-V2.5: 51.9 (#99)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5 |
|---|---|---|
| LMArena Non-English | 1439 | 1404 |
| LMArena Chinese | 1513 | 1468 |
| LMArena French | 1471 | 1447 |
| LMArena German | 1460 | 1421 |
| LMArena Japanese | 1457 | 1306 |
| LMArena Korean | 1425 | 1363 |
| LMArena Russian | 1450 | 1395 |
| LMArena Spanish | 1448 | 1416 |
Instruction Following Too close to call
GPT-5.6 Terra: 76.4 (#40), MiMo-V2.5: 75.5 (#60)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5 |
|---|---|---|
| LMArena Instruction Following | 1454 | 1434 |
Long Context Too close to call
GPT-5.6 Terra: 44.4 (#68), MiMo-V2.5: 44.2 (#73)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5 |
|---|---|---|
| LMArena Longer Query | 1451 | 1445 |
Writing & Preference GPT-5.6 Terra leads
GPT-5.6 Terra: 70.2 (#23), MiMo-V2.5: 61.6 (#86)
| Benchmark | GPT-5.6 Terra | MiMo-V2.5 |
|---|---|---|
| LMArena Text | 1447 | 1428 |
| LMArena Creative Writing | 1410 | 1393 |
| LMArena Multi-Turn | 1449 | 1445 |
| EQ-Bench Creative Writing | 1855 | — |
| EQ-Bench 4 | 1234 | — |
Frequently asked questions
Is GPT-5.6 Terra better than MiMo-V2.5?
GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 43.4 on the Noometry Index. MiMo-V2.5 costs 26× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Which is cheaper, GPT-5.6 Terra or MiMo-V2.5?
MiMo-V2.5 is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; GPT-5.6 Terra lists at $2 and $12.
Is GPT-5.6 Terra or MiMo-V2.5 better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.7 versus 43.9 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Terra does, with 1.05M tokens against 1.05M.
How many benchmarks do GPT-5.6 Terra and MiMo-V2.5 share?
23 benchmarks have published results for both models. GPT-5.6 Terra has 52 scored results on Noometry and MiMo-V2.5 has 23.